Exploring Learning Pattern in Social Network

The advancement of social blog, Tweeter and Face Book has improvised the broad consumer to influence the public opinion and feasibility of brands. Therefore opinion mining and related verticals are of equal importance in prediction as well as in social networked paradigms. We envisage different learning pattern of learning in social network in the form colouring (a social differentiation task) and consensus (a social agreement task) in a networked setting. We investigate the influence that subjects have on their network neighbours and the collective outcome, and find that it varies considerably. The basis of social network information is uncertain in nature hence the learning pattern becomes unsupervised and it takes substantial time to differentiate and aggregate un likely to other conventional network based learning. The paper also explores the possibilities of automated flavour of learning, and decision making with in case consumer consensus on web, which broadly solicits social networking based decision support activities.